Synthetic neural networks for process control
Computers and Industrial Engineering
Using autocorrelations, cusums and runs rules for control chart pattern recognition: an expert system approach
Design of a knowledge-based expert system for statistical process control
Computers and Industrial Engineering
Out-of-control pattern recognition and analysis for quality control charts using LISP-based systems
Computers and Industrial Engineering
The nature of statistical learning theory
The nature of statistical learning theory
Computers and Industrial Engineering
Automated unnatural pattern recognition on control charts using correlation analysis techniques
Computers and Industrial Engineering
A neural network based model for abnormal pattern recognition of control charts
Computers and Industrial Engineering
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks
Computers and Industrial Engineering
Artificial neural networks to classify mean shifts from multivariate χ2 chart signals
Computers and Industrial Engineering
An efficient star acquisition method based on SVM with mixtures of kernels
Pattern Recognition Letters
Feature-based recognition of control chart patterns
Computers and Industrial Engineering
A hybrid system for SPC concurrent pattern recognition
Advanced Engineering Informatics
Expert Systems with Applications: An International Journal
Recognition of control chart patterns using improved selection of features
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
A control chart pattern recognition system using a statistical correlation coefficient method
Computers and Industrial Engineering
Features extraction and analysis for classifying causable patterns in control charts
Computers and Industrial Engineering
A hybrid learning-based model for on-line detection and analysis of control chart patterns
Computers and Industrial Engineering
Kernel based support vector machine via semidefinite programming: Application to medical diagnosis
Computers and Operations Research
International Journal of Computer Integrated Manufacturing
Fault diagnosis in assembly processes based on engineering-driven rules and PSOSAEN algorithm
Computers and Industrial Engineering
Computers and Industrial Engineering
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Multilayer perceptron, fuzzy sets, and classification
IEEE Transactions on Neural Networks
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Statistical process control charts have been widely utilized for monitoring process variation in many applications. Nonrandom patterns exhibited by control charts imply certain potential assignable causes that may deteriorate the process performance. Though some effective approaches to recognition of control chart patterns (CCPs) have been developed, most of them only focus on recognition and analysis of single patterns. A hybrid approach by integrating wavelet transform and improved particle swarm optimization-based support vector machine (P-SVM) for on-line recognition of concurrent CCPs is developed in this paper. A statistical correlation coefficient is used to determine whether the input pattern is a single or concurrent CCP. Based on wavelet transform, a raw concurrent pattern signal is decomposed into two basic pattern signals, which can be recognized by multiclass SVMs. The performance of the hybrid approach is evaluated by simulation experiments, and numerical and graphical results are provided to demonstrate that the proposed approach can perform effectively and efficiently in on-line CCP recognition task.